PFN-TS converts PFN posterior predictives into mean-reward samples for Thompson sampling using a subsampled predictive CLT, with consistency proofs, regret bounds, and strong empirical performance on synthetic and real bandit benchmarks.
URL https://dl.acm.org/doi/10.1145/2641190.2641198
4 Pith papers cite this work. Polarity classification is still indexing.
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TabGRAA enables self-improving tabular language models through iterative group-relative advantage alignment using modular automated quality signals like distinguishability classifiers.
FLOWGEM generates complete data under non-monotone MAR missingness by discretizing Wasserstein gradient flows with a local linear density-ratio estimator to minimize expected KL divergence over missingness patterns.
Quantum kernel methods show no statistically significant edge over strong classical baselines on tabular classification tasks, with current feature maps failing to match the spectral properties of the best classical kernel.
citing papers explorer
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PFN-TS: Thompson Sampling for Contextual Bandits via Prior-Data Fitted Networks
PFN-TS converts PFN posterior predictives into mean-reward samples for Thompson sampling using a subsampled predictive CLT, with consistency proofs, regret bounds, and strong empirical performance on synthetic and real bandit benchmarks.
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Self-Improving Tabular Language Models via Iterative Group Alignment
TabGRAA enables self-improving tabular language models through iterative group-relative advantage alignment using modular automated quality signals like distinguishability classifiers.
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Generative Modeling under Non-Monotone MAR Missingness via Approximate Wasserstein Gradient Flows
FLOWGEM generates complete data under non-monotone MAR missingness by discretizing Wasserstein gradient flows with a local linear density-ratio estimator to minimize expected KL divergence over missingness patterns.
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Benchmarking Quantum Kernel Support Vector Machines Against Classical Baselines on Tabular Data: A Rigorous Empirical Study with Hardware Validation
Quantum kernel methods show no statistically significant edge over strong classical baselines on tabular classification tasks, with current feature maps failing to match the spectral properties of the best classical kernel.